Comprehensive Course Catalog

This table provides a detailed overview of all courses offered across the 8 semesters of the Computer Science and Engineering program at Goel Group of Institutions.

Advanced Departmental Electives

These advanced courses are designed to deepen student understanding in specialized areas of CSE and provide hands-on experience with cutting-edge technologies.

Machine Learning (CSE501)

This course covers supervised and unsupervised learning techniques, including decision trees, neural networks, clustering algorithms, and reinforcement learning. Students will gain practical experience using frameworks like TensorFlow and PyTorch to build and deploy ML models on real-world datasets.

Cybersecurity (CSE502)

Students explore cryptographic protocols, network security mechanisms, ethical hacking techniques, and incident response strategies. This course includes lab sessions where students practice vulnerability assessment and penetration testing using tools like Kali Linux, Wireshark, and Metasploit.

Data Science (CSE503)

This elective introduces students to data visualization, statistical modeling, and big data processing. Using Python and R, students learn how to extract insights from large datasets, apply machine learning algorithms, and communicate findings effectively.

Embedded Systems (CSE601)

This course focuses on designing and developing embedded software for microcontrollers and real-time systems. Students gain experience working with ARM processors, RTOS environments, and sensor integration to create intelligent devices.

Internet of Things (CSE602)

Students explore IoT architecture, protocols, and applications in smart cities, agriculture, healthcare, and industrial automation. Practical components include building prototype systems using Raspberry Pi, Arduino, and cloud platforms like AWS IoT Core.

Human Computer Interaction (CSE603)

This course teaches principles of usability engineering, user research methods, and interface design. Students conduct usability studies, prototype interfaces, and evaluate interaction designs using both qualitative and quantitative approaches.

Deep Learning (CSE504)

Advanced topics in deep learning include convolutional neural networks, recurrent networks, transformers, and generative adversarial networks. Students work on projects involving image recognition, natural language processing, and computer vision applications.

Reinforcement Learning (CSE505)

This course explores the theoretical foundations of reinforcement learning and its applications in robotics, game AI, and autonomous systems. Students implement algorithms like Q-learning and policy gradients to solve complex decision-making problems.

Big Data Analytics (CSE506)

Students learn how to process and analyze large-scale datasets using Hadoop, Spark, and NoSQL databases. The course includes real-time data streaming, predictive analytics, and scalable machine learning techniques for enterprise-level applications.

Cloud Computing (CSE507)

This elective covers cloud architecture, deployment models, security considerations, and service offerings from AWS, Azure, and GCP. Students deploy applications on cloud platforms and learn about DevOps practices in cloud environments.

Project-Based Learning Philosophy

The department places a strong emphasis on project-based learning to ensure that students acquire practical skills and apply theoretical knowledge in real-world scenarios. Projects are structured as follows:

• Mini Projects: Conducted during the second and third years, these projects allow students to work individually or in small teams on focused problems related to core subjects.
• Capstone Projects: In the final two semesters, students undertake full-scale projects that integrate multiple disciplines and technologies. These projects are often sponsored by industry partners or initiated by faculty mentors.

Evaluation criteria include project documentation, presentation quality, technical implementation, innovation level, and teamwork effectiveness. Faculty mentors guide students throughout the process, ensuring alignment with academic standards and industry expectations.